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Various studies have been conducted in the fields of sustainable operations management (SOM), optimization, and wastewater treatment, yielding unsubstantiated recovery. In the context of Europe's climate neutrality vision, this paper reviews effective decarbonization strategies and proposes sustainable approaches to mitigate carbonization in various sectors such as buildings, energy, industry, and transportation and how these interlink with wastewater management. The study also explores the role of digitalization in decarbonization and reviews policies that can direct governments' actions towards a climate-neutral society. This paper presents a review of optimization approaches applied in the fields of science and technology, incorporating modern optimization techniques based on various peer-reviewed published research papers. It emphasizes non-conventional energy and distributed power-generating systems along with the deregulated and regulated environment. Additionally, this paper critically reviews the performance and capability of the micellar-enhanced ultrafiltration (MEUF) process in the treatment of dye wastewater. The review presents evidence of the simultaneous removal of co-existing pollutants and explores the feasibility and efficiency of biosurfactants instead of chemical surfactants. Lastly, the paper proposes a novel Firm–Regulator–Consumer-Technology Enablers/Facilitators interaction framework to study operations, decisions and interactive cooperation considering the relationships between the four agents through a comprehensive literature review of SOM. The proposed framework provides support for exploring future research opportunities and holistic sustainability initiatives. [ABSTRACT FROM AUTHOR]

Moving towards regional Supergrids, an increasing number of interconnections are formed by High Voltage Direct Current (HVDC) lines. Currently, in most regions, HVDC losses are not considered in market operations, resulting in additional costs for Transmission System Operators (TSOs). Nordic TSOs have proposed the introduction of HVDC loss factors in the market clearing algorithm, to account for the cost of losses and avoid HVDC flows between zones with zero price difference. In this paper, we introduce a rigorous framework to assess the introduction of loss factors, in particular HVDC loss factors, in nodal and zonal pricing markets. First, we focus on the identification of an appropriate loss factor. We propose and compare three different models: constant, linear, and piecewise linear. Second, we introduce formulations to include losses in market clearing algorithms. Carrying numerical tests for a whole year, we find that accounting only for HVDC or AC losses may lead to lower social welfare for a non-negligible amount of time. To counter this, this paper introduces a framework for including both AC and HVDC losses in a zonal or nodal pricing environment. We show both theoretically and through simulations that such a framework is guaranteed to increase social welfare. [ABSTRACT FROM AUTHOR]

In this paper we are researching the optimality of developing counties for investing in their energy sector as a way of diversifying portfolio by applying the multicriteria decision making model There are multiple quantitative and qualitative criteria that can be considered when finding the adequate market for investment, other than its natural potential, such as the level that its energy sector is developed, legal framework that surrounds this sector, market openness of the observed economy, ease of investment and market liquidity. The four sources of the energy sector that are considered are oil, gas, coal and renewable energy sources. Even though the renewable energy sources aren't sufficiently exploited, the countries that have high potential could provide significant financial profits by exploiting them. Through this study we are using the most significant and impacting criteria in order to explore and deduce the developing country that has the highest potential of the energy sector and represents the most optimal market to invest. [ABSTRACT FROM AUTHOR]

In the recent era of smart city ecosystems and the Internet of Things (IoT), innovative, intelligent parking systems must make cities more sustainable. Every year, the increasing number of city vehicles requires more time to search for parking slots. In large cities, 10% of the traffic congestion occurs because of cruising; drivers spend almost 20 minutes searching for free space to park their vehicles. The passing time of waiting for parking in the traffic leads the issues such as energy, pollution, and stress. There needs to be more than the developed solutions. Therefore, the necessary to create a parking slot availability detection system that informs the drivers in advance about the free parking slot based on location. This paper introduces an enhanced ensemble Deep Learning (DL) model designed to forecast parking slot availability through the integration of IoT, cloud technology, and sensor networks. The devised model, known as Ensemble CNN-Boosted Graph LSTM (ECNN-BGLSTM), is optimized using a Genetic Algorithm (GA) framework. The model's performance is rigorously evaluated using a dataset from Europe, and various metrics, including Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE), are employed for assessment. The experimental findings demonstrate the superior performance of the proposed model compared to existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In this paper, we present a two-stage model of zonal electricity markets with day-ahead market clearing, and real-time re-dispatch and balancing that accounts for transmission line switching at both stages. We show how the day-ahead problem with switching can be formulated as an adaptive robust optimization problem with mixed integer recourse, and present a new algorithm for solving the adversarial max-min problem that obeys the structure of an interdiction game. We apply the model on a realistic instance of the Central Western European system, and comment on the impacts of both proactive, and reactive transmission switching on the operating costs of the system. Part I presents day-ahead models of a short-term zonal electricity market with switching, and describes our algorithmic approach for solving these models efficiently. Part II describes variants of the real-time model, and presents the results of our case study on the Central Western European market. [ABSTRACT FROM AUTHOR]

The paper addresses a real-time optimization-based reference calculation integrated with a control structure for Modular Multilevel Converters (MMC) operating under normal and constrained situations (where it has reached current and/or voltage limitations, as it may occur during system faults). Firstly, a nonlinear optimization problem has been developed in which it prioritizes to satisfy the AC grid current set-points imposed by the transmission System Operator (TSO). The constrained nonlinear optimization problem is formulated based on the steady-state model of the MMC, whereby the prioritization is achieved through distinct weights defined in the Objective Function’s (OF) terms. The resultant optimization problem, however, is highly nonlinear requiring high computation burden to be solved in real-time. To cope with this issue, this paper applies a Linear Time-Varying (LTV) approximation, which permits to represent the nonlinear dynamics of the system as constant parameters, while a Linear Time-Invariant (LTI) system is used to formulate the optimization constraints. The converter’s current references are determined in real-time by solving a constrained linearized optimization problem at each control time step, which considers the TSO’s demands, the current MMC operating point and its physical limitations. Theoretical analyses comparing the responses of the linear and nonlinear optimization problems are performed to validate the accuracy of the LTV approximation. Finally, the linearized-optimization problem is integrated with the MMC controllers, evaluated under different AC and DC network conditions and compared with conventional control strategies, where it is shown that the presented method can be potentially employed to obtain the MMC current references for distinct network scenarios. [ABSTRACT FROM AUTHOR]

The need to optimize the deployment and maintenance costs for service delivery in wireless networks is an essential task for each service provider. The goal of this paper was to optimize the number of service centres (gNodeB) to cover selected customer locations based on the given requirements. This optimization need is especially emerging in emerging 5G and beyond cellular systems that are characterized by a large number of simultaneously connected devices, which is typically difficult to handle by the existing wireless systems. Currently, the network infrastructure planning tools used in the industry include Atoll Radio Planning Tool, RadioPlanner and others. These tools do not provide an automatic selection of a deployment position for specific gNodeB nodes in a given area with defined requirements. To design a network with those tools, a great deal of manual tasks that could be reduced by more sophisticated solutions are required. For that reason, our goal here and our main contribution of this paper were the development of new mathematical models that fit the currently emerging scenarios of wireless network deployment and maintenance. Next, we also provide the design and implementation of a verification methodology for these models through provided simulations. For the performance evaluation of the models, we utilize test datasets and discuss a case study scenario from a selected district in Central Europe. [ABSTRACT FROM AUTHOR]

Determining contingency aware dispatch decisions by solving a security-constrained optimal power flow (SCOPF) is challenging for real-world power systems, as the high problem dimensionality often leads to impractical computational requirements. This problem becomes more severe when the SCOPF has to be solved not only for a single instance, but for multiple periods, e.g. in the context of electricity market analyses. This paper proposes an algorithm that identifies the minimal set of constraints that exactly define the space of feasible nodal injections for a given network and contingency scenarios. By internalizing the technical limits of the nodal injections and enforcing a minimal worst-case impact of contingencies to line flows, computational effort can be further improved. The case study applies and analyzes the methods on the IEEE 118 and A&M 2000 bus systems, as well as the German and European transmission systems. In all tested cases the proposed algorithm identifies at least 95% of the network and security constraints as redundant, leading to significant SCOPF solve time reductions. Scalability and practical implementation are explicitly discussed. The code and input data of the case study is published supplementary to the paper under an open-source license. [ABSTRACT FROM AUTHOR]

A novel model for the clearing of European day-ahead electricity markets, with all associated types of orders, namely simple hourly orders, block orders, complex orders and PUN (“Prezzo Unico Nazionale”) orders is presented in this paper. An iterative algorithm is employed for the market clearing, bearing an inner iteration process for the handling of Paradoxically Accepted block and MIC orders, and an outer iteration process for the handling of PUN orders. During the iterative process a Master Problem and a PUN subproblem are sequentially solved and coordinated, for the efficient handling of all order clearing conditions and market and/or system constraints. The clearing conditions of each order type are explicitly incorporated in the PUN subproblem, formulating a Mixed Complementarity Problem bearing both primal and dual decision variables. The PUN subproblem concerns the sequential clearing of 24 hourly subproblems, employing a special technique for handling the possible inefficiencies due to the myopic nature of the hourly subproblem solutions, leading to enhanced behavior in terms of social surplus with respect to the state-of-the-art day-ahead market clearing solver. The model and the algorithmic process are evaluated in a west-European test case, demonstrating minimal computational requirements and proving its excellent escalation capabilities. [ABSTRACT FROM AUTHOR]

With Europe dedicated to limiting climate change and greenhouse gas emissions, large shares of Renewable Energy Sources (RES) are being integrated in the national grids, phasing out conventional generation. The new challenges arising from the energy transition will require a better coordination between neighboring system operators to maintain system security. To this end, this paper studies the benefit of exchanging primary frequency reserves between asynchronous areas using the Supplementary Power Control (SPC) functionality of High-Voltage Direct-Current (HVDC) lines. First, we focus on the derivation of frequency metrics for asynchronous AC systems coupled by HVDC interconnectors. We compare two different control schemes for HVDC converters, which allow for unilateral or bilateral exchanges of reserves between neighboring systems. Second, we formulate frequency constraints and include them in a unit commitment problem to ensure the N-1 security criterion. A data-driven approach is proposed to better represent the frequency nadir constraint by means of cutting hyperplanes. Our results suggest that the exchange of primary reserves through HVDC can reduce up to 10% the cost of reserve procurement while maintaining the system N-1 secure. [ABSTRACT FROM AUTHOR]

Securing profits from energy, reserve capacity and balancing markets is critical to ensure the profitability of battery energy systems (BES). However, the intimate connection between offers on these trading floors combined with the limited energy storage capacity of BES renders its scheduling very complex. In this paper, we develop a bilevel optimization problem for strategic participation of a BES in the day-ahead energy-reserve and balancing markets, improving the state-of-the-art by (i) considering the conditional-value-at-risk; (ii) ensuring the real-time feasibility of the obtained day-ahead schedule; (iii) addressing the operational underperformance risk stemming from inaccurate battery modeling. In a case study, we illustrate how the proposed model allows risk-averse BES owners to hedge their day-ahead position without jeopardizing their expected profit, while ensuring the feasibility of their day-ahead schedule. [ABSTRACT FROM AUTHOR]

This article analyzes the temporal programming of sales for a horticultural marketing company, e.g. a cooperative. The empirical study references the European tomato market, where most of the production is sold through the retail channel dominated by large distribution chains. We study the marketing schedule for an individual company, or even a prominent farmer, using a modified Markowitz model, assuming that his decisions do not affect the balance of market prices. As a result, this model can manage risk and improve decision-making. The data also provide information on the risk borne by marketers depending on their sales calendar, which often depends on their geographic location. [ABSTRACT FROM AUTHOR]

In this paper, shaped continuous parallel plate waveguide delay lenses are proposed. The previous configurations introduced by the authors displayed a residual side lobe imbalance, showing the limitations of elliptical delay lens profiles, derived from a starting bifocal constrained lens. Polynomial profiles are proposed here as a way to enhance the scanning performance and the pattern shape. Optimized configurations demonstrate a reduction in the phase aberration levels over a large scanning range ([−30°, 30°]). The associated radiation patterns demonstrate lower and more balanced first side lobe levels (SLLs) ($\cong -18$ dB), as compared to the previous elliptical approach ($\cong -13.2$ dB). A validation has been proposed through the manufacturing and test of a prototype over the down-link Ka-band ([27.5–31] GHz). Excellent radiation performance [half-power beamwidth (HPBW), SLL] has been experimentally obtained over a wide angular range ([−31.5°, 31.5°]), including low scanning loss and high radiation stability over the entire frequency range. High radiation efficiencies are demonstrated with this fully metallic design, particularly suitable for space applications. The mechanical simplicity offered by the concept is also quite attractive for low-cost multi-beam platforms. [ABSTRACT FROM AUTHOR]

Transitioning from fossil fuel dominated power systems to high penetrations of intermittent renewable generation is affecting classical electricity market designs. Here, a method is proposed to model and assess the absence of co-optimization of energy and reserve that prevails in the European system. Co-optimization is formulated through an optimization problem (COM), and its absence as an equilibrium problem (EQM) built on Karush-Kuhn-Tucker conditions of agents’ optimization and market clearing equations. EQM cannot be reformulated as a single optimization problem. Market distortions are identified by comparing the complementarity conditions of both models. These are then discussed on system with Feed in Premium to wind generation. Parameters in the models allow to represent different market configurations regarding: available wind generation, Feed in Premium to wind, generators’ risk aversion, and required reserve from wind generation. [ABSTRACT FROM AUTHOR]

In situations where it is necessary to consider a variety of options when making decisions in forestry (and in general), with the choice influenced by hardly comparable criteria and a number of conflicting interests, a possible solution is to use multiple criteria methods. One of these methods, which can be applied in forestry, is mathematical programming (in particular, linear programming). Linear programming has a long tradition of being used in the U.S. and European forestry, whereas in the forestry of Serbia it still represents a theoretically and practically unknown tool. Therefore, in this paper we analyze the possibility of applying the methods of linear programming in developing a plan of regeneration cutting in the poplar plantations of FMU "Topolik" managed by PE "Vojvodinašume." Using the aimed function (linear programming) and the corresponding software package the maximum yield that can be achieved by cutting the plantation was obtained. The planned management period was from 2012 to 2021 and its volume was 155 852 m3. The preset condition that the yield in half-periods remains equal was fulfilled (halfperiod I 77,925 m3, half-period II 77,927 m3). The maximum yield obtained with this methodology was by 4,040 m3 lower than the theoretically possible yield that would be obtained if all stands were cut down at the end of the second half-period, i.e. higher by 8,430 m3 than the yield that would be obtained if cutting of the stands were performed at the start of the management period. The results obtained and foreign experience in this area clearly indicate that linear programming can successfully be used to solve this problem and even more complex problems (than the one presented in this paper) in our local forest practice (multidimensional planning with a series of constraints). [ABSTRACT FROM AUTHOR]

Štore Steel Ltd. is one of the major flat spring steel producers in Europe. Among several hundred steel grades, 70MnVS4 steel is also produced. In the paper optimization of steelmaking of 70MnVS4 steel is presented. 70MnVS4 is a high-strength microalloyed steel which is used for forging of connecting rods in the automotive industry. During 70MnVS4 ladle treatment, the sulfur addition in the melt should be conducted only once. For several reasons the sulfur is repeatedly added and therefore threatening clogging during continuous casting and as such influencing surface defects occurrence and steel cleanliness. Accordingly, the additional sulfur addition was predicted using linear regression and genetic programming. Following parameters were collected within the period from January 2018 to December 2018 (78 consequently cast batches): sulfur and carbon cored wire addition after chemical analysis after tapping, carbon, manganese and sulfur content after tapping, time between chemical analysis after tapping and starting of the casting, ferromanganese and ferrosilicon addition and additional sulfur cored wire addition. Based on modelling results it was found out that the ferromanganese is the most influential parameter. Accordingly, 12 consequently cast batches (from February 2019 to October 2019) were produced with as lower as possible addition of ferromanganese. The additional sulfur addition in all 12 cases was not needed. Also, the melt processing time, surface quality of rolled material and sulfur cored wire consumption did not change statistically significantly after reduction of ferromanganese addition. The steel cleanliness was statistically significantly better. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal method for estimating time-dependent climate change signals from general circulation models. The basic idea is to identify vectors that maximize the mean-square detection statistic derived from optimal fingerprinting techniques. The method also provides an objective and systematic procedure for identifying the limit to which a signal can be restricted in space and time without losing detectability. As an illustration, the method is applied to the Coupled Model Intercomparison Project, phase 3 multimodel dataset to determine the continental seasonal-mean anomaly in surface air temperature and precipitation that is most detectable, on average, in these models. Anomalies in seasonal-mean surface air temperature are detectable in all seasons by almost all models on all continents but Europe; seasonal-mean anomalies over Europe are undetectable for some models, though this does not preclude other expressions of the signal, such as those that include longer time averages or time-lag information, from being detectable. Detectability in seasonal-mean temperature is found not only for multidecadal warming trends but also for cooling after major volcanic eruptions. In contrast, seasonal-mean precipitation anomalies are detectable in only a few models for averages over 5 yr or more, suggesting that the signal should include more spatiotemporal detail to be detectable across more models. Nevertheless, of the precipitation anomalies that are detectable, the signal appears to be of two characters: a systematic trend and enhanced frequency of extreme values. These results derived from twentieth-century simulations appear to be consistent with previous studies based on twenty-first-century simulations with larger signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

Perturbed physics ensembles (PPEs) have been widely used to assess climate model uncertainties and have provided new estimates of climate sensitivity and parametric uncertainty in state-of-the-art climate models. So far, mainly global climate models were used to generate PPEs, and little work has been conducted with regional climate models. This paper discusses the parameter uncertainty in two PPEs of a regional climate model driven by reanalysis data for the present climate over Europe. The uncertainty is evaluated for the variables of 2-m temperature, precipitation, and total cloud cover, with a focus on the annual cycle, interannual variability, and selected extremes. The authors show that the simulated spread of the PPEs encompasses the observations at a regional scale in terms of the annual cycle and the interannual variability, provided observational uncertainty is taken into account. To rank the PPEs a new skill metric is proposed, which takes into account observational uncertainty and natural variability. The metric is a generalization of the climate prediction index (CPI) and is compared to metrics used in other studies. The consideration of observational uncertainty is particularly important for total cloud cover and reveals that current observations do not allow for a systematic evaluation of high precipitation intensities over the entire European domain. The skill framework is additionally used to identify important model parameters, which are of interest for an objective model calibration. [ABSTRACT FROM AUTHOR]

The European biodiesel industry is currently facing several challenges affecting the profitability of investment projects in the industry. Among these challenges are higher prices for oilseeds, which are the main input for biodiesel production, lower fiscal support by national governments for biodiesel producers and high price volatility of oil markets. Thus identifying all opportunities for optimizing the value chain and lower the production cost of biodiesel is a main requirement for an efficient project management in the biodiesel industry. The paper addresses this topic by developing a decision support system tailored to the needs of Romanian investors in biodiesel production. The system optimizes the main activities of the biodiesel value chain and supports the decision making process at management level. In addition the DSS enables the user to perform sensitivity analysis based on varying various input parameters. [ABSTRACT FROM AUTHOR]

This paper builds a dynamic programming model to optimize the collections process in consumer credit. It determines which collections actions should be undertaken and how long they should be performed, including theoretical results about the form of the optimal policy under certain conditions. Finally, a case study is described based on data from the collections department of a European bank. [ABSTRACT FROM AUTHOR]

The challenge in vehicle and crew scheduling is to arrange a given set of bus trips into running boards while minimizing certain objectives and complying with a given set of constraints. This scheduling was solved several tens years ago manually by a dispatcher who used his skill, experience, knowledge of history, and intuition. This attitude worked quite well in small instances but could not be applied in larger ones. Development of computers brought opportunities to build computerized vehicle and crew optimization systems. This paper describes a computer system KASTOR developed for vehicle and crew optimization, which complies with the special requirements of Czech and Slovak bus providers, and is significantly different from those in some west European countries. [ABSTRACT FROM AUTHOR]

Network constraints play a key role in the price finding mechanism for European Power Markets, but historical data is very sparse and usually insufficient for many quantitative applications. We reconstruct the constraints data, known as the Power Transmission Distribution Factors (PTDFs) and Remaining Available Margins (RAMs), by first recovering the underlying time dependent signals known as the Generation Shift Keys (GSKs) and Phase Angles (PAs), and the electricity grid characteristics, via a mathematical optimisation problem. This is solved by exploiting marginal convexity in certain subspaces via alternating minimisation. The GSKs and PAs are then mapped to the PTDFs and RAMs, using the grid structure. Our reconstruction achieves in-sample relative errors of 22.3% and 15.9% for the PTDFs and RAMs respectively, while the out-of-sample ones are 25.4% and 18.1% respectively. We further show that our model outperforms the naive approach, and that the reconstructed GSKs and PAs recover specific structure. [ABSTRACT FROM AUTHOR]

Štore Steel Ltd. is one of the biggest flat spring steel producers in Europe. The main motive for this study was to study the influences of non-metallic inclusions on mechanical properties obtained by tensile testing. From January 2016 to December 2021, all available tensile strength data (472 cases–472 test pieces) of 17 low alloy steel grades, which were ordered and used by the final user in rolled condition, were gathered. Based on the geometry of rolled bars, selected chemical composition, and average size of worst fields non-metallic inclusions (sulfur, silicate, aluminium and globular oxides), determined based on ASTM E45, several models for tensile strength, yield strength, percentage elongation, and percentage reduction area were obtained using linear regression and genetic programming. Based on modeling results in the period from January 2022 to April 2022, five successively cast batches of 30MnVS6 were produced with a statistically significant reduction of content of silicon (t-test, p < 0.05). The content of silicate type of inclusions, yield, and tensile strength also changed statistically significantly (t-test, p < 0.05). The average yield and tensile strength increased from 458.5 MPa to 525.4 MPa and from 672.7 MPa to 754.0 MPa, respectively. It is necessary to emphasize that there were no statistically significant changes in other monitored parameters. [ABSTRACT FROM AUTHOR]

With the current European tendency to gradually increase the contribution of renewable energy to almost the entire production, the rate of wind farm constructions is at an all-time high. As the locations for the wind turbines are dictated by factors usually independent of soil conditions (average wind speeds, possibility of connecting to existing electrical networks, approval of authorities and local population), it is not uncommon for the geotechnical conditions to be unfavorable. In these circumstances, the choice of the optimal foundation system is an important aspect in the design phase of a wind turbine. The aim of this paper is to analyze the suitability of the available foundation solutions (shallow foundation on natural or improved ground or piled foundation - classical or piled raft) for a number of idealized soil profiles and varying loads. The study is highly parametric, as all foundation solutions are analyzed for a range of realistic tower loads and different (including extremely difficult) soil conditions. The expected conclusions are general statements about favorable conditions for each foundation system and the expected failure criteria. [ABSTRACT FROM AUTHOR]

Virtual powertrain analysis is widely applied in the automotive industry to cope with the increasing complexity and variance of future vehicle propulsion technologies. Since the vehicle-usage behavior has a strong impact on component loads, realistic computation results require accurate assumptions about these boundaries. In this context, driving cycles (DCs) are used to represent the system boundaries in vehicle operation. The aim of this article is to identify multiple characteristic driving cycles (CDCs) from extensive vehicle measurement data which represent the full variety of possible real-driving scenarios. Vehicle measurements are segmented and consumption-relevant features are extracted from each segment. These features are then used to apply clustering and classification techniques to identify characteristic groups that are consequently assigned to different driving environments and driving styles. In order to obtain even more realistic driving scenarios, a data-fusion approach is used to incorporate a road slope signal from a NASA digital elevation model for each segment. Lastly, a genetic mixed-integer optimization algorithm is proposed to efficiently generate representative DCs for each characteristic group of driving segments. The main contribution of this article is a data-driven identification of the parameter space of real-driving scenarios from extensive vehicle measurements including the implementation of road slope information. The scenarios are represented via a constrained number of compact CDCs which enables comprehensive investigations of new powertrain technologies under average as well as extreme real-driving conditions to develop efficiency-robust powertrain systems. [ABSTRACT FROM AUTHOR]

The single imbalance pricing is an emerging mechanism in European electricity markets where all positive and negative imbalances are settled at a unique price. This real-time scheme thereby stimulates market participants to deviate from their schedule to restore the power system balance. However, exploiting this market opportunity is very risky due to the extreme volatility of the real-time power system conditions. In order to address this issue, we implement a new tailored deep-learning model, named encoder-decoder, to generate improved probabilistic forecasts of the imbalance signal, by efficiently capturing its complex spatio-temporal dynamics. The predicted distributions are then used to quantify and optimize the risk associated with the real-time participation of market players, acting as price-makers, in the imbalance settlement. This leads to an integrated forecast-driven strategy, modeled as a robust bi-level optimization. Results show that our probabilistic forecaster achieves better performance than other state of the art tools, and that the subsequent risk-aware robust dispatch tool allows finding a tradeoff between conservative and risk-seeking policies, leading to improved economic benefits. Moreover, we show that the model is computationally efficient and can thus be incorporated in the very-short-term dispatch of market players with flexible resources. [ABSTRACT FROM AUTHOR]

An important source of model uncertainty in climate models arises from unconfined model parameters in physical parameterizations. These parameters are commonly estimated on the basis of manual adjustments (expert tuning), which carries the risk of overtuning the parameters for a specific climate region or time period. This issue is particularly germane in the case of regional climate models (RCMs), which are often developed and used in one or a few geographical regions only. This study addresses the role of objective parameter calibration in this context. Using a previously developed objective calibration methodology, an RCM is calibrated over two regions (Europe and North America) and is used to investigate the transferability of the results. A total of eight different model parameters are calibrated, using a metamodel to account for parameter interactions. The study demonstrates that the calibration is effective in reducing model biases in both domains. For Europe, this concerns in particular a pronounced reduction of the summer warm bias and the associated overestimation of interannual temperature variability that have persisted through previous expert tuning efforts and are common in many global and regional climate models. The key process responsible for this improvement is an increased hydraulic conductivity. Higher hydraulic conductivity increases the water availability at the land surface and leads to increased evaporative cooling, stronger low cloud formation, and associated reduced incoming shortwave radiation. The calibrated parameter values are found to be almost identical for both domains; that is, the parameter calibration is transferable between the two regions. This is a promising result and indicates that models may be more universal than previously considered. [ABSTRACT FROM AUTHOR]

A 1.2-meter reflectarray antenna has been designed to accomplish the requirements of a Direct Broadcast Satellite (DBS) mission, which provides a South America transmit-receive coverage in Ku band. The antenna has been designed by applying first of all a pattern synthesis technique to obtain the required phase distribution on the reflectarray at several frequencies in transmit and receive bands. Then, the patch dimensions have been optimized in a configuration made of three stacked layers of varying-sized patches in order to provide the required phase distribution at transmit and receive frequencies. An antenna demonstrator has been manufactured and tested. The measured patterns are in good agreement with the simulations and they are close to fulfill the coverage requirements in both transmit and receive bands. [ABSTRACT FROM PUBLISHER]

Europe stated the ambitious target of becoming carbon neutral by 2050 to combat climate change and meet the requirements imposed by the Paris Agreement, and renewable energy has proved to be a promising solution for the decarbonization of many sectors. Nonetheless, their aleatory nature leads to grid unbalances due to the difference between supply and demand. Storage solutions are needed, and electrofuels become a key factor in this context: they are fuels produced from electricity, which leads to carbon-neutral fuels if it originates from renewable sources. These can constitute a key solution to store the surplus energy and to decarbonize the so-called hard-to-abate sectors. Electrofuel production technologies have not yet been fully developed, and, in this context, extensive study of the state-of-the-art of existing projects can be very useful for researchers and developers. This work researches the European projects funded by the Horizon 2020 Programme regarding electrofuel production. The projects were analyzed in-depth using specific features, and the results were presented. [ABSTRACT FROM AUTHOR]

Štore Steel Ltd. is one of the major flat spring steel producers in Europe. Until 2016 the company used a three-strand continuous casting machine with 6 m radius, when it was replaced by a completely new two-strand continuous caster with 9 m radius. For the comparison of the tensile strength of 41 hypoeutectoid steel grades, we conducted 1847 tensile strength tests during the first period of testing using the old continuous caster, and 713 tensile strength tests during the second period of testing using the new continuous caster. It was found that for 11 steel grades the tensile strength of the rolled material was statistically significantly lower (t-test method) in the period of using the new continuous caster, whereas all other steel grades remained the same. To improve the new continuous casting process, we decided to study the process in more detail using the Multiple Linear Regression method and the Genetic Programming approach based on 713 items of empirical data obtained on the new continuous casting machine. Based on the obtained models of the new continuous casting process, we determined the most influential parameters on the tensile strength of a product. According to the model's analysis, the secondary cooling at the new continuous caster was improved with the installation of a self-cleaning filter in 2019. After implementing this modification, we performed an additional 794 tensile tests during the third period of testing. It was found out that, after installation of the self-cleaning filter, in 6 steel grades out of 19, the tensile strength in rolled condition improved statistically significantly, whereas all the other steel grades remained the same. [ABSTRACT FROM AUTHOR]